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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Semantic Graph & Ontology Architect - **Company:** Rubicon Consulting - **Location:** Four Ashes, UK - **Experience:** Expert - **Contract:** Temporary contract - **Skills:** Query Performance, Application Programming Interfaces (APIs), Amazon Web Services, Amazon S3, Business Logic, Audit Trail, Data Validation, Data Governance, Graph Database, Identity and Access Management, MongoDB, Neo4j, NoSQL, Query Optimization, Role-Based Access Control, Prometheus, Search Technologies, Microsoft SharePoint, SPARQL, Management of Software Versions, Enterprise Data Management, Data Ingestion, Snowflake, Grafana, Amazon Virtual Private Cloud (VPC) - **Published:** June 12, 2026 - **Apply:** https://www.apply4u.co.uk/jobs/x/38811684/ ## About the Role * Bachelor's/Master's in CS, Data Science, Mathematics, Engineering, or related field. * 7-12 years in graph databases, semantic modelling, ontology engineering. * Deep expertise in Cypher, Gremlin, SPARQL; strong command of LPG vs RDF/OWL tradeoffs. * Hands-on with Neo4j, AWS Neptune, TigerGraph, Stardog (at least one in production). * Experience mapping enterprise data (Snowflake/MongoDB/SharePoint/ERP) into graph/ontology layers. * Strong understanding of RBAC/RACI, data governance, lineage, and security controls. * Ability to design clean APIs and reference implementations for semantic enrichment/retrieval. * Practical AWS familiarity (IAM, VPC, S3, EKS/ECS/Lambda) in collaboration with platform teams. Preferred Qualifications * Ontology tooling (Protégé, SHACL/SWRL), reasoning engines, and constraint modeling. * Prior delivery of enterprise knowledge graphs supporting workflows & audit trails. * Exposure to vector retrieval/RAG and how graph context informs re-ranking. * Observability awareness (tracing across graph layers, OpenTelemetry, Prometheus/Grafana). * Experience with Snowflake/MongoDB/SharePoint APIs and ERP data structures ## Description We're building a Smart Data Fabric that unifies enterprise data (Snowflake, SharePoint, ERP, NoSQL, and document silos) and exposes it to advanced AI agents through a semantic, graph-native, and vector-aware foundation. You will own graph and semantic architecture, modeling business relationships, processes, and logic to enable accurate, contextual, auditable workflows. This is a hands-on leadership role spanning LPG vs RDF/OWL tradeoffs, query optimization, and ontology engineering., Graph & Semantic Architecture * Design scalable graph schemas (LPG and/or RDF/OWL) based on semantic and inference requirements. * Author and optimize Cypher/Gremlin/SPARQL queries for multi-hop traversal, orchestration, and complex reasoning. * Define canonical entity models and mapping layers across Snowflake, MongoDB, SharePoint, ERP, and unstructured content. Ontology Engineering & Reasoning * Create and maintain formal ontologies/taxonomies; govern versioning and lifecycle. * Implement logical inference (rules/constraints) for agent decision-making, conflict detection, and workflow integrity. * Establish semantic consistency standards and data quality checks. Hybrid Semantic Layer (Graph + Logic) * Design a hybrid semantic layer combining graph context with business logic and access controls for semantic search, multi-hop traversal, and knowledge contextualization. * Model RACI/RBAC as graph edges/nodes; embed compliance rules and auditability. APIs, Patterns & Collaboration * Define clean API layers for semantic enrichment and retrieval; deliver reference implementations and patterns. * Specify MCP-based (Model Context Protocol) tool discovery/invocation patterns; collaborate with platform engineers for agent connectivity. * Partner with data/platform/security teams on ingestion pipelines, lineage, governance, and observability requirements. Quality, Performance & Governance * Set query performance budgets; prevent Cartesian explosions; ensure index utilization. * Establish lineage and data governance artifacts (semantic catalogs, policy nodes, audit trails). * Document standards and mentor engineers adopting graph/semantic patterns. ## Related Videos - [Scaling GraphRAG: Efficient Knowledge Retrieval for AI](https://www.wearedevelopers.com/videos/100025-scaling-graphrag-efficient-knowledge-retrieval-for-ai) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) - [Your organization as a Graph](https://www.wearedevelopers.com/videos/2051-your-organization-as-a-graph) - [Graphs and RAGs Everywhere... But What Are They? - Andreas Kollegger - Neo4j](https://www.wearedevelopers.com/videos/1311-graphs-and-rags-everywhere-but-what-are-they-andreas-kollegger-neo4j) ## Related Articles - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Everything a Developer Needs to Know About MCP with Neo4j](https://www.wearedevelopers.com/magazine/604-everything-a-developer-needs-to-know-about-mcp-with-neo4j) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [Dev Digest 139 - Soft and hard queries](https://www.wearedevelopers.com/magazine/487-dev-digest-139-soft-and-hard-queries) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story)